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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You need a model to track weekly cohort retention, with the ability to slice data across multiple dimensions. The underlying data is voluminous. Which approach lays a good foundation for scalability?

A) A wide table with columns for each potential filtering combination (country, product category, acquisition channel, etc.).
B) Introduce a JSON column to store dimensions dynamically, reducing the number of columns.
C) Normalize the cohort data and dimensions, using joins for analysis.
D) Pre-calculate all possible cohort and dimension combinations as an incremental model.

2. You've finished a new feature but realize some related models now have minor formatting inconsistencies (e.g., spacing, indentation). What's the best way to handle this alongside your feature changes?

A) Use git add -p to stage only the feature-related changes, and leave formatting for a future task.
B) Address the formatting issues in the same commits as the feature to avoid extra work.
C) Create a separate branch and commit addressing only the formatting.
D) Briefly explain in your pull request description that some incidental formatting changes were included

3. You're working with a development data warehouse meant to closely mirror production. To make code more portable between environments, you opt to use environment variables for connection details. What's the best way to manage these?

A) Set environment variables manually in your shell session prior to running dbt commands.
B) Use a dedicated secrets manager integrated with your dbt project.
C) Define environment variables as part of your CI/CD tool's job configuration.
D) Store sensitive credentials in plain text in the dbt_project.yml file, relying on file system permissions.

4. You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?

A) Create a macro, encapsulating the date calculations, and reference it in the affected models.
B) Introduce a new intermediate table to store the result of the date calculations.
C) Continue copying the date calculation logic into each relevant model.
D) Write a custom Python function and import it into your dbt project.

5. (Multiple Select)

A) test-paths
B) database
C) materialized: view
D) model-paths

Solutions: